Some work was also done on generating point clouds from single images such as PointSetGeneration [1]} and DensePCR [2]}.
In addition, DensePCR also upsamples the generated point cloud using multiple stages .
But all these above networks only consider point clouds as output.
Point normals are also an important property for many applications from surface reconstruction to rendering.
Using classical techniques, normal calculation are subjective to noise and outliers.
In this paper, we propose to predict not only the point positions but also the corresponding normals using deep learning.
